MLflow Machine Learning Platform

An MLflow Machine Learning Platform is a open-source machine learning (ML) model management platform.

• Context:
  • It can include an ML Experiment Tracking System: MLflow Tracking.
  • It can include an ML Project Codebase Management System: MLflow Projects.
  • It can include an ML Model Deployment Management System: MLflow Models.
  • It can include an ML Model Store System: MLflow Model Registry.
  • It can be a member of a Databricks Platform.
  • …
• Example(s):
  • MLflow v1.9.0 (~2020-06-19) [1] [2].
  • MLflow v1.7.0 (~2020-03-02) [3] [4].
  • …
• Counter-Example(s):
  • Seldon Core.
  • Pipeline.AI.
  • Data Version Control (DVC) Framework.
• See: ML Model Deployment, Mleap, ML Model Management Platform.

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References

2020a

• https://medium.com/@ravishankar.nair/online-and-batch-based-ml-execution-from-same-python-code-preserving-pre-and-post-transformation-ea7ebc27f50f
  • QUOTE: How does the overall deployment looks like? It should support both real time and batch based. A minimal representation is:

    

2020b

• https://mlflow.org/docs/latest/index.html
  • QUOTE: MLflow is an open source platform for managing the end-to-end machine learning lifecycle. It tackles four primary functions:
    • Tracking experiments to record and compare parameters and results (MLflow Tracking).
    • Packaging ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production (MLflow Projects).
    • Managing and deploying models from a variety of ML libraries to a variety of model serving and inference platforms (MLflow Models).
    • Providing a central model store to collaboratively manage the full lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations (MLflow Model Registry).
  • MLflow is library-agnostic. You can use it with any machine learning library, and in any programming language, since all functions are accessible through a REST API and CLI. For convenience, the project also includes a Python API, R API, and Java API.

2020c

• https://www.g2.com/products/mlflow/reviews
  • QUOTE:
    • 1) A single format to support all measure ML libraries such as Sklearn, Tensorflow, MXnet, Spark MLlib, Pyspark etc.
    • 2) Capabilities to deploy on Amazon Sagemaker with just one API call
    • 3) Flexibility to log all model params such as Accuracy, Recall, etc. along with Hyperparameter tuning support.
    • 4) A good GUI to compare and select the best models.
    • 5) Model registry to track Staging, Production, and Archived models.

2019a

• https://slideshare.net/databricks/mlflow-10-meetup
  • QUOTE: MLflow 1.0 is coming soon as the first stable release of MLflow. It also packs many cleanups and improvements, such as simpler metadata management, search APIs and HDFS support. In this talk, we’ll present these new features in detail, and then discuss additional MLflow components that Databricks and other companies are working on for the rest of 2019. ...

2019b

• https://mlflow.org/
  • QUOTE: ... MLflow (currently in beta) is an open source platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. It currently offers three components:
    • MLflow Tracking: Record and query experiments: code, data, config, and results.
    • MLflow Projects: Packaging format for reproducible runs on any platform.
    • MLflow Models: General format for sending models to diverse deployment tools.

2019c

• https://databricks.com/blog/2019/10/17/introducing-the-mlflow-model-registry.html
  • QUOTE: ... MLflow already has the ability to track metrics, parameters, and artifacts as part of experiments, package models and reproducible ML projects, and deploy models to batch or real-time serving platforms.

    The MLflow Model Registry builds on MLflow’s existing capabilities to provide organizations with one central place to share ML models, collaborate on moving them from experimentation to testing and production, and implement approval and governance workflows. Since we started MLflow, model management was the top requested feature among our open source users, so we are excited to launch a model management system that integrates directly with MLflow. ...

    

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